Department of Cellular and Molecular Medicine, University of California, San Diego, CA, 92093, USA.
Department of Cellular and Molecular Medicine, University of California, San Diego, CA, 92093, USA; Department of Medicine, University of California, San Diego, CA, 92093, USA.
Trends Immunol. 2023 Dec;44(12):954-964. doi: 10.1016/j.it.2023.10.006. Epub 2023 Nov 7.
Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.
单细胞方法已经将离散的特定组织巨噬细胞状态作为焦点，对其进行了最细微的解构。机器学习 (ML) 方法最近通过揭示跨越组织共享的无上下文连续状态，对这一观点提出了挑战。这两种方法都认为，“刹车”和“加速器”巨噬细胞亚群必须保持平衡，才能实现体内平衡。这两种方法还强调了群体流变性的重要性，因为亚群在广泛的加速器和刹车表型之间切换，以对任何威胁做出最优化的整体反应。全面理解这些刹车和加速器状态的规则是一个很有前途的途径，因为它可以帮助制定精确的巨噬细胞再教育治疗策略，可能选择性地增强或抑制各种组织中与疾病相关的状态和表型。
